Meta Interview Questions

Meta Behavioral & Leadership Interview Questions

Practice 1,166 real Meta interview questions for 2026. Covers top categories — Coding & Algorithms, Analytics & Experimentation, Data Manipulation (SQL/Python), Behavioral & Leadership, and System Design — across Software Engineer, Data Scientist, Machine Learning Engineer, Data Engineer, and Product Manager roles. Real questions from actual interviews with detailed solutions. Expect a software-engineering-heavy loop: timed algorithmic coding (trees, arrays, graph/maze problems, delimiter/CSV parsing), system-design prompts like leaderboards, flight search and online-judge architectures, and an increasingly common AI-assisted coding round that mirrors real workflows. Data Scientist rounds emphasize product analytics and experimentation—designing tests, diagnosing spend drops and bots, evaluating unconnected content, and writing SQL for multi-account, seller, and vehicle metrics. Machine Learning Engineer questions skew toward recommender and ranking work (place and friend recommendation, sparse-matrix ops, linear-regression derivations, newsfeed dislike models). Data Engineers focus on data modeling, ETL, capacity calculations, reservations/utilization queries, and production SQL/Python tasks. For interview preparation, prioritize timed coding practice, system-design templates, rigorous SQL drills (joins/CTEs/aggregation), clear A/B-testing frameworks, and concise STAR behavioral stories tied to measurable impact.

1.2k Questions 1 Company08.08.2026
Showing 20 results
Role
Meta logo
Meta
Medium
Data Scientist

Design A/B Test to Evaluate New Video-Feed Feature

Design A/B Test to Evaluate New Video-Feed Feature Scenario A consumer social-media app is launching a short‑video feed (TikTok-style). A newly added ...

Analytics & Experimentation
2
0
28 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Convince Leadership to Launch Group Chat Feature

Convince Leadership to Launch Group Chat Feature Evaluating a Group Chat / Group Video-Call Feature for Instagram Context You are a Data Scientist ask...

Analytics & Experimentation
6
0
43 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze Recent User Activity from Video Call Logs

video_calls caller | recipient | ds | call_id | duration 123 | 456 | 2019-01-01 | 4325 | 864.4 032 | 789 | 2019-01-01 | 9395 | 263.7 456 | 032 | 2019-...

Data Manipulation (SQL/Python)
0
0
7 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Design Metrics to Track and Analyze Spam Impact

Design Metrics to Track and Analyze Spam Impact Scenario A messaging product team wants to reduce spam without harming normal user experience. You do ...

Analytics & Experimentation
6
0
51 people solved
Aug 4, 2025
Meta logo
Meta
Easy
Data Scientist

Determine Significance of Model B's Performance Improvement

Determine Significance of Model B's Performance Improvement A/B Test: Two-Proportion Z-Test for Success Rates Scenario You ran an A/B test comparing t...

Analytics & Experimentation
3
0
28 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Identify Algorithms for Detecting Malicious Duplicated Content

Identify Algorithms for Detecting Malicious Duplicated Content Detecting Malicious Duplicated Text (DOT) Scenario You are selecting technical approach...

Machine Learning
6
0
47 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Estimate Fake Accounts Using Data Signals and Sampling

Estimate Fake Accounts Using Data Signals and Sampling Estimating Fake Accounts on a Social Network Background A large social platform wants to estima...

Analytics & Experimentation
5
0
42 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Optimize Travel Costs and Generate Rotational Symmetric Numbers

Scenario You are building a travel-search engine that must 1) show customers the cheapest round-trip they can book if departure and return prices vary...

Coding & Algorithms
8
0
59 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Estimate Instagram Shopping Feature's Revenue and Test Impact

Estimate Instagram Shopping Feature's Revenue and Test Impact Instagram Shopping: Sizing, Experiment Design, and Troubleshooting Context Instagram is ...

Analytics & Experimentation
3
0
41 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze View Distribution and Recommendation Overlap in Videos

Analyze View Distribution and Recommendation Overlap in Videos Short-Video Platform: View Distribution and Recommendation Overlap Context You are anal...

Statistics & Math
7
0
54 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate Classifier with Precision, Recall, and Fairness Metrics

Evaluate Classifier with Precision, Recall, and Fairness Metrics Offline Evaluation Framework for a Harmful-Content Video Classifier Context You are e...

Machine Learning
5
0
45 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Design Metrics to Measure Inappropriate Content Severity and Prevalence

Design Metrics to Measure Inappropriate Content Severity and Prevalence Harmful-Content Detection: Measurement Plan and Experiment Design Objective Yo...

Analytics & Experimentation
3
0
29 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Improve Team Dynamics: Addressing Unwelcoming Behavior Effectively

Improve Team Dynamics: Addressing Unwelcoming Behavior Effectively Behavioral & Leadership (Meta, Data Scientist) — Onsite Scenario You are interviewi...

Behavioral & Leadership
4
0
33 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze Video View Distribution: Mode, Median, Mean Comparison

Analyze Video View Distribution: Mode, Median, Mean Comparison Scenario You are analyzing user engagement on a short-video sharing product. The team n...

Statistics & Math
65
0
142 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist Locked

Design an Experiment to Evaluate New Recommendation Model

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a rea...

Analytics & Experimentation
138
2
376 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Pivot Projects Quickly and Foster Team Inclusion

Pivot Projects Quickly and Foster Team Inclusion Meta Data Scientist Onsite — Behavioral & Leadership (STAR) Scenario You’ve joined a cross‑functional...

Behavioral & Leadership
27
0
81 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Identify Features for Fake News Detection on Facebook

Identify Features for Fake News Detection on Facebook Design a Machine-Learning System to Flag Fake News on Facebook Scenario An increase in fake news...

Machine Learning
32
0
98 people solved
Aug 4, 2025
Meta logo
Meta
Easy
Data Scientist

Calculate Posterior Fraud Probability Using Bayes' Theorem

Calculate Posterior Fraud Probability Using Bayes' Theorem Posterior Fraud Probability After a Flag Context You operate a fraud detection system that ...

Statistics & Math
19
0
87 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Diagnose Causes of Low Retention for FB Light

Diagnose Causes of Low Retention for FB Light Diagnose Low Retention for FB Light (Android-only, Emerging Markets) Context You are a data scientist on...

Analytics & Experimentation
37
0
73 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze Top Call Initiators and Active French Video Callers

calls +---------+-----------+-------------+---------------------+---------+-----------+ | call_id | caller_id | receiver_id | call_start_time | co...

Data Manipulation (SQL/Python)
87
0
229 people solved
Aug 4, 2025

Frequently Asked Questions

How difficult are Meta interview questions?
Meta interview questions span a wide difficulty range because they must screen candidates from entry to senior levels across many functions. Expect coding rounds to map to medium-to-hard algorithmic problems that appear in top 100 problem lists for software engineers, and expect data roles to face challenging SQL, experiment diagnosis, and product-analytics problems that require clean metric definitions. Machine learning roles emphasize recommendation and ranking tradeoffs and model complexity, while data engineers encounter large-scale ETL and modeling puzzles. Difficulty scales with level: entry hires see clearer, bounded problems; senior hires face ambiguous tradeoffs and system-wide thinking.
What is Meta's interview process and where do these questions appear?
Meta typically runs a multi-stage process: recruiter screen, one or two technical screens or an online assessment, a full loop of onsite-style interviews, then debrief, committee review, and offer. The full loop mixes coding, system or product design, role-specific technical rounds, and behavioral interviews. Software-engineer candidates spend most time on coding and design; data scientists focus on SQL, experimentation, and product analytics; machine-learning engineers see modeling and recommendation design; data engineers handle SQL, data modeling, and pipeline questions; PMs get product-design and analytics probes. In 2025–2026 some teams pilot AI-enabled coding rounds.
How should I structure a preparation timeline for a Meta interview?
A focused six-week plan works well: weeks one and two cover fundamentals—data structures, algorithms, SQL basics, and experiment design; weeks three and four emphasize timed problem practice, mock phone screens, and role-specific cases (A/B diagnosis for data scientists, model design for MLEs, ETL modeling for data engineers); week five concentrates on system or product design and behavioral storytelling; week six is for full mock loops, timing, and refining communication. Practice with realistic tools, simulate loop pacing, and schedule a debrief after each mock to iterate on clarity, edge-case handling, and time management.
Which technical subtopics are most commonly tested for each role at Meta?
For Data Scientist interviews the recurring technical themes are product-metric definition, diagnosing experiment and spend drops, counting multi-account interactions, SQL for multi-entity metrics, and ranking or recommendation evaluation such as shop ad ranking. Software-engineer questions frequently focus on timestamped state and versioned systems, leaderboards and ranking, maze/graph traversal and tree/array transforms, delimiter and CSV parsing, and scalable search or flight-search style designs. Machine-learning engineers see place and friend recommendation design, sparse-matrix operations, ranking/loss choices, and feed dislike or personalization models. Data engineers repeatedly face entity modeling for feed and booking data, SQL analytics for utilization and reservations, and capacity-aware aggregation challenges.
What standout tips and common pitfalls should I watch for in Meta interviews?
Start interviews by clarifying requirements and expected outputs, then propose measurable success metrics; this prevents misaligned solutions. For coding, think aloud, handle edge cases, state complexity up front, and write a couple of quick tests. In design rounds quantify load, storage, and tradeoffs rather than vague features. Data roles must define metrics, guardrails, and experiment assumptions before jumping to analysis; common pitfalls are ambiguous metric definitions, peeking at tests, and ignoring instrumentation limits. For AI-assisted coding rounds, use the assistant to accelerate boilerplate but validate logic and corner cases yourself. Finish each answer with a concise summary of impact and tradeoffs.

Explore more Meta interview questions

Jump straight to Meta questions for a specific role or category.

By role
By category
In-depth guides
Across all companies

Featured Meta interview prep guides

Concept walkthroughs, worked examples, and the real questions from candidate reports.

Editorial prep
Product Manager
Meta interview
Read the guide
Editorial prep
Software Engineer
Meta interview
Read the guide